Users often desire to gather information about entities of interest, such as companies, schools, etc. There have been some recent efforts to automate this task by leveraging the vast corpus of hypertext markup language (HTML) tables available via the Internet. Such efforts are often referred to as entity augmentation. Accuracy of entity augmentation depends on semantic relationships between web tables and semantic labels of those tables. Current techniques work well for string-valued, e.g., textual, and static attributes, but current techniques perform poorly for numeric and time-varying attributes. While numeric and/or time-varying information may be available, they are often provided in different units or for different periods of time. Thus, while existing techniques may be well suited to string-values and static attributes, they will often return incorrect information for numeric and/or time-varying attributes. The inaccuracy and need for error correction make information gathering tasks for numeric and time-varying attributes extremely labor-intensive today.